8  Zooarchaeology

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8.1 Case studies

The following map shows the sites under investigation, divided by chronology. Please select the desired chronology (or chronologies) from the legend on the right.

Legend: R = Roman, LR = Late Roman, EMA = Early Middle Ages, Ma = 11th c. onwards

8.2 Medians

These medians were calculated before switching to bayesian methods. The new quantifications are in the section Chronology, so this section has probably to be removed.

The faunal dataset is large (434+ records) and diversified. Looking at the distributions of each animal, the curve is not gaussian. The best choice for non-normal curves is to use medians instead of means to come up with figures that are less dependent on outliers. The function Medians_Chrono_Zoo() (Section 3.1) can be used to return as output weighted medians for each chronology. The in-depth description of how weights are calculated for each sample can be found in Section 6.4.1. To summarise, sites with a very large (i.e. fuzzy) chronology contribute less to the calculation of the median. Table 8.1 provides the median values of the main categories of faunal remains for each chronology, and Table 8.2 the median values for each century. Stronger colours in the cells indicate higher values.

Show the code
Medians_Categorised_per_Chronology_ZOO <- 
  data.frame(
    Medians_Chrono_Zoo(zooarch_cond, "R")*100,
    Medians_Chrono_Zoo(zooarch_cond, "LR")*100,
    Medians_Chrono_Zoo(zooarch_cond, "EMA")*100,
    Medians_Chrono_Zoo(zooarch_cond, "Ma")*100
  )

# Round to 2 digits
Medians_Categorised_per_Chronology_ZOO <- round(Medians_Categorised_per_Chronology_ZOO, 2)

## Weighted medians per century ##
Medians_ZOO_Centuries <- data.frame(
  "I BCE" = zooarch_tables(zooarch_cond, -1)$Medians,  
  "I CE" = zooarch_tables(zooarch_cond, 1)$Medians,
  "II CE" = zooarch_tables(zooarch_cond, 2)$Medians,
  "III CE" = zooarch_tables(zooarch_cond, 3)$Medians,
  "IV CE" = zooarch_tables(zooarch_cond, 4)$Medians,
  "V CE" = zooarch_tables(zooarch_cond, 5)$Medians,
  "VI CE" = zooarch_tables(zooarch_cond, 6)$Medians,
  "VII CE" = zooarch_tables(zooarch_cond, 7)$Medians,
  "VIII CE" = zooarch_tables(zooarch_cond, 8)$Medians,
  "IX CE" = zooarch_tables(zooarch_cond, 9)$Medians,
  "X CE" = zooarch_tables(zooarch_cond, 10)$Medians,
  "XI CE" = zooarch_tables(zooarch_cond, 11)$Medians
)

# Assigning the colnames (optional - instead of roman numerals)
colnames(Medians_ZOO_Centuries) <- c("1st c. BCE", "1st c. CE", "2nd c.", "3rd c.", "4th c.", "5th c.", "6th c.", "7th c.", "8th c.", "9th c.", "10th c.", "11th c.")

# Rounding the medians
Medians_ZOO_Centuries <- round(Medians_ZOO_Centuries, digits=2)

# Removing categories that are not necessary
Medians_ZOO_Centuries <- Medians_ZOO_Centuries[-c(6:9),]
Table 8.1: Weighted medians of zooarchaeological remains, divided by chronology.
Chronologies
R LR EMA Ma
Pigs 48.00 38.62 35.00 35.62
Cattle 10.75 8.36 18.00 19.00
Caprine 25.00 22.08 29.00 27.00
Dom..Fowl 4.00 6.00 5.00 5.00
Edible.W..Mammals 5.00 3.00 3.00 3.00
Fish 1.00 2.00 3.93 1.00
Mollusca 11.00 8.00 4.00 2.89
Unedible.Dom..Mammals 2.00 3.00 3.95 2.00
Unedible.Wild.Mammals 1.00 1.00 1.00 1.00

Pigs’ medians from the Italian peninsula are the highest in each chronology, although their values decrease after the Roman age peak. Cattle medians slightly decrease after the Roman age, even though surprisingly (put a reference here to literature review to explain why surprisingly) the values increase again (18–19.71%) during the early Medieval and Medieval age. The trends for sheeps and goats are also interesting. During the Roman age the Italian median is 25%, slightly decreasing in the 3rd to the 5th century, and increasing again after. When discussing sheep-farming, one must always consider the geographical features from which the data is being collected. This will be discussed later on in the chapter, where more regional and geographical trends will be provided. Domestic fowl (chickens and geese) has quite stable values of 4-5%, with a peak of 7.68% in the 11th century. Wild game peaks during the Roman age, with a median value of 5%, reaching a minimum in the early Middle ages (2%) and rising again in the 11th century. Two considerations must be made for game consumption. The first is that as we will see later on, game consumption is strongly related to the site typology. Secondly, the Roman age value is pulled up by assemblages from the 1st century BCE. After that, the values strongly decrease and by looking at the individual centuries the medians from the 7th century onwards are much higher (ranging from 1.42% to 2.09%).

Table 8.2: Weighted medians of zooarchaeological remains, divided by century.
Faunal remains
Pigs Cattle Caprine Domestic fowl Wild game
1st c. BCE 39.70 11.41 26.07 0.00 0.76
1st c. CE 40.89 11.31 25.48 0.00 0.58
2nd c. 48.08 10.28 22.58 0.79 0.00
3rd c. 41.57 8.27 19.42 1.57 0.72
4th c. 34.14 9.58 23.36 1.85 0.97
5th c. 34.05 13.33 24.59 2.33 0.90
6th c. 31.83 20.66 28.55 2.75 0.65
7th c. 30.42 18.52 30.14 4.51 1.58
8th c. 33.63 13.89 30.13 2.70 1.18
9th c. 37.54 11.76 23.32 1.16 1.54
10th c. 35.77 14.36 25.64 1.63 1.93
11th c. 34.08 19.34 27.44 1.91 2.09
* The color gradients in this table are used to indicate the chronologies.

9 Precision plots

The following plots for the posterior distributions of the beta-binomial models will also come with a precision parameter \(\phi\), that shapes the beta curve. What is the correct way to interpret it? A \(\phi\) value smaller than 2 indicates that there is extreme overdispersion in the dataset, and that probabilities around 0 and 1 are more likely than the mean. On the contrary, a \(\phi\) value greater than 2 indicates more precision in the dataset. Although this is often the case of larger datasets, where most of the values shrink towards the average, it also indicates a coherent dataset (where values are similar). Finally, if \(\phi\) equals 2 every probability between 0 and 1 is likely possible.

10 Chronology

11 Context type

11.1 Pigs

Question: Is the animal X - let’s say Pigs - more strongly associated to a particular settlement type during a certain chronology?

Expectation 1: Pigs % should be higher in urban and fortified settlements, as they are animals which are only used for meat and can sustain large populations or the military.

Expectation 2: Possibly pigs would increase in villas in the Late Roman age, as their production shifts to a more extensive agriculture (Source: Historical literature).

Expectation 3: If urban density decreases during the late Roman and early Medieval phase, but increases in the Medieval age, do the pigs % follow similar trends?

To estimate the animals’ occurrences probability in each chronology and context, I used a betabinomial distribution to model overdispersion in the data. The \(A\) on the left side of the formula is the outcome variable—the animal NISP counts for each observation \(i\). This is an intercept-only model, where the intercept \(\alpha\) carries an interaction index \({[TCid]}\) as the model will provide estimates for each context type and chronology under examination. The \(\phi\) parameter indicates the precision in the Beta distribution, modelled by chronology and context type.

\[ A_{i} \sim BetaBinomial(NISP_{i}, \bar{p}_{i} , \phi_{i}) \]

\[ logit(\bar{p}_{i}) = \alpha_{[TCid]} \]

\[ \alpha_{[TCid]} \sim Normal(0,1.5) \]

\[ \phi_{[TCid]} \sim Exponential(1) \]

11.2 Cattle

11.3 Caprine

Warning: The `size` argument of `element_line()` is deprecated as of ggplot2 3.4.0.
ℹ Please use the `linewidth` argument instead.

11.4 Edible W. Mammals

11.5 Community plot

12 Macroregion

To estimate the animals’ occurrences probability in each chronology and macroregion, I used a betabinomial distribution to model overdispersion in the data. The \(A\) on the left side of the formula is the outcome variable—the animal NISP counts for each observation \(i\). This is an intercept-only model, where the intercept \(\alpha\) carries an interaction index \({[REGid]}\) as the model will provide estimates for each macroregion and chronology under examination. The \(\phi\) parameter indicates the precision in the Beta distribution, modelled by chronology and macroregion.

\[ A_{i} \sim BetaBinomial(NISP_{i}, \bar{p}_{i} , \phi_{i}) \]

\[ logit(\bar{p}_{i}) = \alpha_{[REGid]} \]

\[ \alpha_{[REGid]} \sim Normal(0,1.5) \]

\[ \phi_{[REGid]} \sim Exponential(1) \]

12.1 Pigs

12.2 Cattle

12.3 Caprine

12.4 Edible W. Mammals

13 Geography

To estimate the animals’ occurrences probability in each chronology and geography, I used a betabinomial distribution to model overdispersion in the data. The \(A\) on the left side of the formula is the outcome variable—the animal NISP counts for each observation \(i\). This is an intercept-only model, where the intercept \(\alpha\) carries an interaction index \({[GEOid]}\) as the model will provide estimates for each geography and chronology under examination. The \(\phi\) parameter indicates the precision in the Beta distribution, modelled by chronology and geography.

\[ A_{i} \sim BetaBinomial(NISP_{i}, \bar{p}_{i} , \phi_{i}) \]

\[ logit(\bar{p}_{i}) = \alpha_{[GEOid]} \]

\[ \alpha_{[GEOid]} \sim Normal(0,1.5) \]

\[ \phi_{[GEOid]} \sim Exponential(1) \]

13.1 Pigs

13.2 Cattle

13.3 Caprine

13.4 Edible W. Mammals

14 Altitude

The proposed model to estimate the probability of occurrence as related to the altitude (the slope \(\beta\)) and chronology (\({[ChrID]}\)) uses a betabinomial distribution to model overdispersion in the data. The \(A\) on the left side of the formula is the outcome variable—the animal NISP counts for each observation \(i\). This is a simple intercept with slope model, where the intercept \(\alpha\) carries an index \({[ChrID]}\) as the model provides estimates for each chronology under examination. A single \(\phi\) parameter indicates the precision in the Beta distribution.

\[ A_{i} \sim BetaBinomial(NISP_{i}, \bar{p}_{i} , \phi_{i}) \]

\[ logit(\bar{p}_{i}) = \alpha_{[ChrID]} + \beta_{[ChrID]}\cdot Alt_{i} \]

\[ \alpha_{ChrID} \sim Normal(0,1.5) \]

\[ \beta_{ChrID} \sim Normal(0,1.5) \]

\[ \phi \sim Exponential(1) \]

OGR data source with driver: ESRI Shapefile 
Source: "/Users/robertoragno/Desktop/University/Bari/PhD - Quarto/Italy_SHP/ne_110m_coastline/ne_110m_coastline.shp", layer: "ne_110m_coastline"
with 134 features
It has 3 fields
Integer64 fields read as strings:  scalerank 

14.1 Pigs

14.2 Cattle

14.3 Caprine

14.4 Edible W. Mammals

14.5 Community plot

Figure 14.1: MCMC estimates for slope and intercept plotted in the logit scale. Negative slopes indicate a negative relationship between the animal remains and increasing altitude. Intercepts were kept as a baseline occurrence probability of the species. Species on the left of the graph are rarer, species on the right are more common. It is important to notice that this represents the species response to elevation.